21 citations · 30 across the 19 of their papers we have counts for
22 papers
Scaling Unified Multimodal Models to Long-Horizon Interleaved Generation
Ziqiao Peng, Yutao Cui, Wang Lin +12
Unified multimodal models (UMMs) provide an interface for interleaved text-image generation, where generated visual states can directly inform subsequent reasoning and synthesis. H…
CameraEditor: Camera-Controlled Image Editing via Video-Prior Sequential Modeling
Xin Shen, Chengyou Jia, Keshuo Xing +6
Beyond semantic content, camera parameters play a pivotal role in dictating the geometric perspective and appearance of any given image. While recent image editing models excel at…
OmniTryOn: Video Try-On Anything at Once!
Changliang Xia, Chengyou Jia, Minnan Luo +3
Although video virtual try-on (VVT) has achieved significant progress, existing methods still exhibit two fundamental limitations: first, they are restricted to single-garment tran…
-Predictor: Noise-Free Deterministic Diffusion for Dense Prediction
Changliang Xia, Chengyou Jia, Minnan Luo +3
Although diffusion models with strong visual priors have emerged as powerful dense prediction backbones, they overlook a core limitation: the stochastic noise at the core of diffus…
PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling
Bowen Ping, Chengyou Jia, Minnan Luo +4
Consistent image generation requires faithfully preserving identities, styles, and logical coherence across multiple images, which is essential for applications such as storytellin…
CoFFT: Chain of Foresight-Focus Thought for Visual Language Models
Xinyu Zhang, Yuxuan Dong, Lingling Zhang +5
Despite significant advances in Vision Language Models (VLMs), they remain constrained by the complexity and redundancy of visual input. When images contain large amounts of irrele…
Multi-Modal Dataset Distillation in the Wild
Zhuohang Dang, Minnan Luo, Chengyou Jia +3
Recent multi-modal models have shown remarkable versatility in real-world applications. However, their rapid development encounters two critical data challenges. First, the trainin…
Why Settle for One? Text-to-ImageSet Generation and Evaluation
Chengyou Jia, Xin Shen, Zhuohang Dang +6
Despite remarkable progress in Text-to-Image models, many real-world applications require generating coherent image sets with diverse consistency requirements. Existing consistent…
From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios
Changliang Xia, Chengyou Jia, Zhuohang Dang +3
Dense prediction tasks hold significant importance of computer vision, aiming to learn pixel-wise annotated labels for input images. Despite advances in this field, existing method…
AutoGPS: Automated Geometry Problem Solving via Multimodal Formalization and Deductive Reasoning
Bowen Ping, Minnan Luo, Zhuohang Dang +2
Geometry problem solving presents distinctive challenges in artificial intelligence, requiring exceptional multimodal comprehension and rigorous mathematical reasoning capabilities…
PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning
Xinyu Zhang, Yuxuan Dong, Yanrui Wu +6
Large language models demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based re…
ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
Chengyou Jia, Changliang Xia, Zhuohang Dang +3
Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the c…
AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant
Chengyou Jia, Minnan Luo, Zhuohang Dang +5
Digital agents capable of automating complex computer tasks have attracted considerable attention due to their immense potential to enhance human-computer interaction. However, exi…
Disentangled Noisy Correspondence Learning
Zhuohang Dang, Minnan Luo, Jihong Wang +6
Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impract…
Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation
Zhuohang Dang, Minnan Luo, Chengyou Jia +3
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. Recently, to alleviate expensive data collection, co-occurring pairs from the Inte…
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition
Chengyou Jia, Minnan Luo, Xiaojun Chang +6
Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing method…
Disentangled Representation Learning with Transmitted Information Bottleneck
Zhuohang Dang, Minnan Luo, Chengyou Jia +4
Encoding only the task-related information from the raw data, \ie, disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Al…
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement
Chengyou Jia, Minnan Luo, Zhuohang Dang +3
Dominant Person Search methods aim to localize and recognize query persons in a unified network, which jointly optimizes two sub-tasks, \ie, pedestrian detection and Re-IDentificat…
SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation
Chengyou Jia, Minnan Luo, Zhuohang Dang +4
Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls. In contrast, Layout-to…
Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History
Bo Yu, Hechang Chen, Chengyou Jia +5
Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A serie…
Disentangled Generation with Information Bottleneck for Few-Shot Learning
Zhuohang Dang, Jihong Wang, Minnan Luo +3
Few-shot learning (FSL), which aims to classify unseen classes with few samples, is challenging due to data scarcity. Although various generative methods have been explored for FSL…
CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search
Chengyou Jia, Minnan Luo, Caixia Yan +2
Recently, weakly supervised person search is proposed to discard human-annotated identities and train the model with only bounding box annotations. A natural way to solve this prob…